[Expert consensus on ultrasound guided thyroid and neck lymph node puncture (2023 edition)].

Thyroid nodule and cervical lymph node biopsy is the main clinical method for evaluating the condition and determining the follow-up treatment plan. The literature on thyroid nodule puncture predominantly focuses on thyroid fine needle puncture, and there are limited systematic articles on coarse needle aspiration for thyroid-related diseases and needle biopsy of thyroid-related cervical lymph node diseases. However, this shortage of articles does not reflect the diagnostic value of coarse needle aspiration in thyroid biopsy and cervical lymph node-related diseases. Currently, different departments of many hospitals in China are conducting or planning to perform needle biopsy of thyroid and cervical lymph node-related diseases to improve the standardization and safety of related operations. Standardization is needed for the indications, contraindications, perioperative period, postoperative complications management, puncture specimen processing, and related genetic analysis of thyroid and cervical lymph node puncture. For this purpose, Interventional Ultrasound Committee of Chinese College of Interventionalists organized a panel of domestic experts in the field of thyroid diseases to discuss and formulate a consensus. Based on the latest research progress, combined with the clinical realities in China, this Expert Consensus on Ultrasound Guided Thyroid and Neck Lymph Node Puncture (2023 edition) is released.

Indocyanine green near-infrared fluorescence-assisted video endoscopic inguinal lymph node dissection for rectal cancer: a single-center experience.

BACKGROUND: This study aimed to investigate the safety and feasibility of indocyanine green near-infrared fluorescence (ICG-NIR) fluorescence-guided video-endoscopic inguinal lymphadenectomy (VEIL) for rectal cancer with inguinal lymph node metastasis (ILNM). METHODS: A retrospective analysis was conducted on 11 patients with rectal cancer who underwent ICG-NIR fluorescence-guided VEIL, assessing various parameters such as operation time, intraoperative bleeding, number of harvested lymph nodes, intraoperative and postoperative complications, and follow-up. RESULTS: Regarding surgical procedures for ILNM, unilateral surgery was performed in 7 cases (54.5%) and bilateral surgery in 4 cases (45.5%). Among these 15 ICG-NIR-guided VEIL surgeries in 11 patients, positive fluorescence visualization was achieved in 13 operations (86.7%). The median estimated blood loss was 10 ml, and the median operation time was 90 min. One case (6.7%) required conversion to open surgery. The median duration of the drain tube was 12 days, and the median length of postoperative hospital stay was 20 days. Postoperative complications were observed, including incisional infection in 2 cases (18.2%), lymphatic leakage in 5 cases (45.5%), urinary infection in 1 case (9.1%), and pneumonia in 3 cases (27.3%). Complications such as skin necrosis, lower limb venous thrombosis, lower limb swelling, or impaired movement were observed during the postoperative follow-up period. No cases of primary lesion, groin, or pelvic lymph node recurrence were observed. CONCLUSION: ICG-NIR fluorescence-guided VEIL is a safe and feasible surgical treatment for rectal cancer with ILNM. ICG fluorescence guidance holds promise as a more personalized and precise approach for VEIL in rectal cancer surgery.

[18F]FAPI adds value to [18F]FDG PET/CT for diagnosing lymph node metastases in stage I-IIIA non-small cell lung cancer: a prospective study.

BACKGROUND: This study investigates the value of fluorine 18 ([18F])-labeled fibroblast activation protein inhibitor (FAPI) for lymph node (LN) metastases in patients with stage I-IIIA non-small cell lung cancer (NSCLC). METHODS: From November 2021 to October 2022, 53 patients with stage I-IIIA NSCLC who underwent radical resection were prospectively included. [18F]-fluorodeoxyglucose (FDG) and [18F]FAPI examinations were performed within one week. LN staging was validated using surgical and pathological findings. [18F]FDG and [18F]FAPI uptake was compared using the Wilcoxon signed-ranks test. Furthermore, the diagnostic value of nodal groups was investigated. RESULTS: In 53 patients (median age, 64 years, range: 31-76 years), the specificity of [18F]FAPI for detecting LN metastasis was significantly higher than that of [18F]FDG (P < 0.001). High LN risk category, greater LN short-axis dimension(≥ 1.0 cm), absence of LN calcification or high-attenuation, and higher LN FDG SUVmax (≥ 10.1) were risk factors for LN metastasis(P < 0.05). The concurrence of these four risk factors accurately predicted LN metastases (Positive Predictive Value [PPV] 100%), whereas the presence of one to three risk factors was unable to accurately discriminate the nature of LNs (PPV 21.7%). Adding [18F]FAPI in this circumstance improved the diagnostic value. LNs with an [18F]FAPI SUVmax<6.2 were diagnosed as benign (Negative Predictive Value 93.8%), and LNs with an [18F]FAPI SUVmax≥6.2 without calcification or high-attenuation were diagnosed as LN metastasis (PPV 87.5%). Ultimately, the integration of [18F]FDG and [18F]FAPI PET/CT resulted in the highest accuracy for N stage (83.0%) and clinical decision revisions for 29 patients. CONCLUSION: In patients with stage I-IIIA NSCLC, [18F]FAPI contributed additional valuable information to reduce LN diagnostic uncertainties after [18F]FDG PET/CT. Integrating [18F]FDG and [18F]FAPI PET/CT resulted in more precise clinical decisions. TRIAL REGISTRATION: The Chinese Clinical Trial Registry: ChiCTR2100044944 (Registered: 1 April 2021, https://www.chictr.org.cn/showprojEN.html?proj=123995 ).

Extraneural metastatic ependymoma: distant metastasis to the pleura, lungs, lymph nodes and bone.

Ependymomas are neuroepithelial tumours arising from ependymal cells surrounding the cerebral ventricles that rarely metastasise to extraneural structures. This spread has been reported to occur to the lungs, lymph nodes, liver and bone. We describe the case of a patient with recurrent CNS WHO grade 3 ependymoma with extraneural metastatic disease. He was treated with multiple surgical resections, radiation therapy and salvage chemotherapy for his extraneural metastasis to the lungs, bone, pleural space and lymph nodes.

Analysis on efficacy of magnetic resonance lymphangiography using INV-001 in healthy beagle dogs.

We aimed to conduct a proof-of-concept study of INV-001 in visualizing lymphatic vessels and nodes without venous contamination and to determine the optimal dose condition of INV-001 for magnetic resonance lymphangiography (MRL) in healthy beagles. MRL was performed using a 3.0-Tesla (T) whole body clinical magnetic resonance imaging (MRI) scanner. A dose-finding study of INV-001 for MRL in beagles (N = 6) was carried out according to an adaptive optimal dose finding design. For the reproducibility study (N = 6), MRL was conducted at selected INV-001 doses (0.056 and 0.112 mg Fe/kg) with a 15 mM concentration. Additionally, an excretion study (N = 3) of INV-001 was conducted by analyzing T1, T2, and T2* maps of the liver and kidney 48 h post-administration. INV-001 administration at doses of 0.056 and 0.112 mg Fe/kg (concentration: 15 mM) consistently demonstrated the visualization of contrast-enhanced lymphatic vessels and nodes without venous contamination in the beagles. The contrast enhancement effect was highest at 30 min after INV-001 administration, then gradually decreasing. No toxicity-related issues were identified during the study. After 48 h, the T1, T2, and T2* values in the liver and both kidneys were found to be comparable to the pre-administration values, indicating thorough INV-001 excretion. The optimal dosing conditions of INV-001 for MRL for contrast-enhanced visualization of lymphatic vessels and nodes exclusively with no venous contamination in beagles was determined to be 0.056 mg Fe/kg with a 15 mM concentration.

Head-to-head comparison of [68Ga]Ga-DOTA-FAPI-04 and [18F]FDG PET/CT for the evaluation of tonsil cancer and lymph node metastases: a single-centre retrospective study.

BACKGROUND: This study aimed to compare the diagnostic value of [68 Ga]Ga-DOTA-FAPI-04 and [18F]FDG PET/CT imaging for primary lesions and metastatic lymph nodes in patients with tonsil cancer. METHOD: Twenty-one tonsil cancer patients who underwent [68 Ga]Ga-DOTA-FAPI-04 and [18F]FDG PET/CT scans within two weeks in our centre were retrospectively enrolled. The maximum standardized uptake value (SUVmax) and tumor-to-background ratio (TBR) of the two tracers were compared by using the Mann‒Whitney U test. In addition, the sensitivity, specificity, and accuracy of the two methods for diagnosing metastatic lymph nodes were analysed. RESULTS: In detecting primary lesions, the efficiency was higher for [68 Ga]Ga-DOTA-FAPI-04 PET/CT (20/22) than for [18F]FDG PET/CT (9/22). Although [68 Ga]Ga-DOTA-FAPI-04 uptake (SUVmax, 5.03 ± 4.06) was lower than [18F]FDG uptake (SUVmax, 7.90 ± 4.84, P = 0.006), [68 Ga]Ga-DOTA-FAPI-04 improved the distinction between the primary tumor and contralateral normal tonsillar tissue. The TBR was significantly higher for [68 Ga]Ga-DOTA-FAPI-04 PET/CT (3.19 ± 2.06) than for [18F]FDG PET/CT (1.89 ± 1.80) (p < 0.001). In lymph node analysis, SUVmax and TBR were not significantly different between [68 Ga]Ga-DOTA-FAPI-04 and [18F]FDG PET/CT (7.67 ± 5.88 vs. 8.36 ± 6.15, P = 0.498 and 5.56 ± 4.02 vs. 4.26 ± 3.16, P = 0.123, respectively). The specificity and accuracy of [68 Ga]Ga-DOTA-FAPI-04 PET/CT were higher than those of [18F]FDG PET/CT in diagnosing metastatic cervical lymph nodes (all P < 0.05). CONCLUSION: The availability of [68 Ga]Ga-DOTA-FAPI-04 complements the diagnostic results of [18F]FDG by improving the detection rate of primary lesions and the diagnostic accuracy of cervical metastatic lymph nodes in tonsil cancer compared to [18F]FDG.

Effects of concurrent chemoradiotherapy with or without Endostar on the regression of retropharyngeal lymph node and prognosis of patients with locally advanced nasopharyngeal carcinoma: a retrospective study.

BACKGROUND AND PURPOSE: To investigate the effect of combining Endostar with concurrent chemoradiotherapy (ECCRT) compared to concurrent chemoradiotherapy (CCRT) on the regression rate of retropharyngeal lymph nodes (RLNs) and the relationship between regression rate of RLNs and prognosis of patients with locally advanced nasopharyngeal carcinoma (LANPC). METHODS: A total of 122 LANPC patients with RLNs metastasis were included. Metastatic RLNs were delineated both before and after treatment slice by slice on the magnetic resonance images cross-section. The regression rate of RLNs, adverse effects (AE) were evaluated. The median regression rate of RLNs was taken as the cut-off value, and the patients were furtherly divided into high regression rate (HRR) group and low regression rate (LRR) group, then survival times were evaluated. RESULTS: The median regression rates of RLNs in the ECCRT and CCRT groups were 81% and 50%, respectively (P < 0.001). There was no statistically significant difference in the incidence of grade 3/4 AEs between the two groups, except for oral mucositis (ECCRT 26.23% vs. CCRT 44.26%, P = 0.037). The 3-year overall survival (OS), progression-free survival (PFS), distant metastasis-free survival (DMFS) and locoregional failure-free survival (LRFFS) rates in the HRR and LRR groups were 85.48% and 86.67% (P = 0.983), 80.65% and 68.33% (P = 0.037), 83.87% and 85% (P = 0.704), 93.55% and 81.67% (P = 0.033), respectively. CONCLUSIONS: Patients in the ECCRT group had higher regression rates of RLNs and lower incidence of severe oral mucositis. Furthermore, patients in the HRR group had a better 3-year PFS and LRFFS rate than those in the LRR group.

Incidence rate of occult lymph node metastasis in clinical T1-2N0M0 small cell lung cancer patients and radiomic prediction based on contrast-enhanced CT imaging: a multicenter study : Original research.

BACKGROUND: This study aimed to explore the incidence of occult lymph node metastasis (OLM) in clinical T1 - 2N0M0 (cT1 - 2N0M0) small cell lung cancer (SCLC) patients and develop machine learning prediction models using preoperative intratumoral and peritumoral contrast-enhanced CT-based radiomic data. METHODS: By conducting a retrospective analysis involving 242 eligible patients from 4 centeres, we determined the incidence of OLM in cT1 - 2N0M0 SCLC patients. For each lesion, two ROIs were defined using the gross tumour volume (GTV) and peritumoral volume 15 mm around the tumour (PTV). By extracting a comprehensive set of 1595 enhanced CT-based radiomic features individually from the GTV and PTV, five models were constucted and we rigorously evaluated the model performance using various metrics, including the area under the curve (AUC), accuracy, sensitivity, specificity, calibration curve, and decision curve analysis (DCA). For enhanced clinical applicability, we formulated a nomogram that integrates clinical parameters and the rad_score (GTV and PTV). RESULTS: The initial investigation revealed a 33.9% OLM positivity rate in cT1 - 2N0M0 SCLC patients. Our combined model, which incorporates three radiomic features from the GTV and PTV, along with two clinical parameters (smoking status and shape), exhibited robust predictive capabilities. With a peak AUC value of 0.772 in the external validation cohort, the model outperformed the alternative models. The nomogram significantly enhanced diagnostic precision for radiologists and added substantial value to the clinical decision-making process for cT1 - 2N0M0 SCLC patients. CONCLUSIONS: The incidence of OLM in SCLC patients surpassed that in non-small cell lung cancer patients. The combined model demonstrated a notable generalization effect, effectively distinguishing between positive and negative OLMs in a noninvasive manner, thereby guiding individualized clinical decisions for patients with cT1 - 2N0M0 SCLC.

Axillary lymph node metastasis in pure mucinous carcinoma of breast: clinicopathologic and ultrasonographic features.

BACKGROUND: The purpose of this research is to study the sonographic and clinicopathologic characteristics that associate with axillary lymph node metastasis (ALNM) for pure mucinous carcinoma of breast (PMBC). METHODS: A total of 176 patients diagnosed as PMBC after surgery were included. According to the status of axillary lymph nodes, all patients were classified into ALNM group (n = 15) and non-ALNM group (n = 161). The clinical factors (patient age, tumor size, location), molecular biomarkers (ER, PR, HER2 and Ki-67) and sonographic features (shape, orientation, margin, echo pattern, posterior acoustic pattern and vascularity) between two groups were analyzed to unclose the clinicopathologic and ultrasonographic characteristics in PMBC with ALNM. RESULTS: The incidence of axillary lymph node metastasis was 8.5% in this study. Tumors located in the outer side of the breast (upper outer quadrant and lower outer quadrant) were more likely to have lymphatic metastasis, and the difference between the two group was significantly (86.7% vs. 60.3%, P = 0.043). ALNM not associated with age (P = 0.437). Although tumor size not associated with ALNM(P = 0.418), the tumor size in ALNM group (32.3 ± 32.7 mm) was bigger than non-ALNM group (25.2 ± 12.8 mm). All the tumors expressed progesterone receptor (PR) positively, and 90% of all expressed estrogen receptor (ER) positively, human epidermal growth factor receptor 2 (HER2) were positive in two cases of non-ALNM group. Ki-67 high expression was observed in 36 tumors in our study (20.5%), and it was higher in ALNM group than non-ALNM group (33.3% vs. 19.3%), but the difference wasn't significantly (P = 0.338). CONCLUSIONS: Tumor location is a significant factor for ALNM in PMBC. Outer side location is more easily for ALNM. With the bigger size and/or Ki-67 higher expression status, the lymphatic metastasis seems more likely to present.

Diagnosis of cervical lymphoma using a YOLO-v7-based model with transfer learning.

To investigate the ability of an auxiliary diagnostic model based on the YOLO-v7-based model in the classification of cervical lymphadenopathy images and compare its performance against qualitative visual evaluation by experienced radiologists. Three types of lymph nodes were sampled randomly but not uniformly. The dataset was randomly divided into for training, validation, and testing. The model was constructed with PyTorch. It was trained and weighting parameters were tuned on the validation set. Diagnostic performance was compared with that of the radiologists on the testing set. The mAP of the model was 96.4% at the 50% intersection-over-union threshold. The accuracy values of it were 0.962 for benign lymph nodes, 0.982 for lymphomas, and 0.960 for metastatic lymph nodes. The precision values of it were 0.928 for benign lymph nodes, 0.975 for lymphomas, and 0.927 for metastatic lymph nodes. The accuracy values of radiologists were 0.659 for benign lymph nodes, 0.836 for lymphomas, and 0.580 for metastatic lymph nodes. The precision values of radiologists were 0.478 for benign lymph nodes, 0.329 for lymphomas, and 0.596 for metastatic lymph nodes. The model effectively classifies lymphadenopathies from ultrasound images and outperforms qualitative visual evaluation by experienced radiologists in differential diagnosis.

A non-invasive preoperative prediction model for predicting axillary lymph node metastasis in breast cancer based on a machine learning approach: combining ultrasonographic parameters and breast gamma specific imaging features.

BACKGROUND: The most common route of breast cancer metastasis is through the mammary lymphatic network. An accurate assessment of the axillary lymph node (ALN) burden before surgery can avoid unnecessary axillary surgery, consequently preventing surgical complications. In this study, we aimed to develop a non-invasive prediction model incorporating breast specific gamma image (BSGI) features and ultrasonographic parameters to assess axillary lymph node status. MATERIALS AND METHODS: Cohorts of breast cancer patients who underwent surgery between 2012 and 2021 were created (The training set included 1104 ultrasound images and 940 BSGI images from 235 patients, the test set included 568 ultrasound images and 296 BSGI images from 99 patients) for the development of the prediction model. six machine learning (ML) methods and recursive feature elimination were trained in the training set to create a strong prediction model. Based on the best-performing model, we created an online calculator that can make a linear predictor in patients easily accessible to clinicians. The receiver operating characteristic (ROC) and calibration curve are used to verify the model performance respectively and evaluate the clinical effectiveness of the model. RESULTS: Six ultrasonographic parameters (transverse diameter of tumour, longitudinal diameter of tumour, lymphatic echogenicity, transverse diameter of lymph nodes, longitudinal diameter of lymph nodes, lymphatic color Doppler flow imaging grade) and one BSGI features (axillary mass status) were selected based on the best-performing model. In the test set, the support vector machines' model showed the best predictive ability (AUC = 0.794, sensitivity = 0.641, specificity = 0.8, PPV = 0.676, NPV = 0.774 and accuracy = 0.737). An online calculator was established for clinicians to predict patients' risk of ALN metastasis ( https://wuqian.shinyapps.io/shinybsgi/ ). The result in ROC showed the model could benefit from incorporating BSGI feature. CONCLUSION: This study developed a non-invasive prediction model that incorporates variables using ML method and serves to clinically predict ALN metastasis and help in selection of the appropriate treatment option.

Prognostic impact of suspicious extraabdominal lymph nodes on patient survival in advanced ovarian cancer.

OBJECTIVE: To evaluate the clinical impact of suspicious extra-abdominal lymph nodes (EALNs) identified preoperatively on CT and/or PET/CT images in advanced ovarian cancer. METHODS: A retrospective study was conducted with 122 patients diagnosed with stage III or IV ovarian cancer with preoperative CT and/or PET/CT images from 2006 to 2022. Imaging studies were evaluated for the presence, size and location of suspicious EALNs. Suspicious lymph node enlargement was defined by a cut-off ≥5mm short-axis dimension on CT and/or lesions with maximum standardized uptake values of ≥2.5 on PET/CT. This study only included patients who did not have their EALNs surgically removed. RESULTS: A total 109 patients met the inclusion criteria; 36 (33%) had suspicious EALNs and were categorized as "node-positive". The median overall survival (OS) was 45.73 months for the "node-positive" and 46.50 months for the "node-negative" patients (HR 1.17, 95% CI 0.68-2.00, p = 0.579). In multivariate analysis, after adjusting for other variables selected by process of backward elimination using a significance level of p<0.20, suspicious EALNs still showed no clinical significance on OS (aHR 1.20, 95% CI 0.67-2.13, p = 0.537) as well as progression-free survival (aHR 1.43, 95% CI 0.85-2.41, p = 0.174). Old age (aHR 2.23, 95% CI 1.28-3.89, p = 0.005) and platinum resistance (aHR 1.92, 95% CI 1.10-3.36, p = 0.023) affects adversely on OS. CONCLUSION: Suspicious EALNs did not worsen the prognosis of patients with advanced ovarian cancer. However, its impact on survival is not yet clarified. Further investigation is required to assess the clinical significance of suspicious EALNs on preoperative imaging studies.

The machine learning-based model for lateral lymph node metastasis of thyroid medullary carcinoma improved the prediction ability of occult metastasis.

BACKGROUND: For medullary thyroid carcinoma (MTC) with no positive findings in the lateral neck before surgery, whether prophylactic lateral neck dissection (LND) is needed remains controversial. A better way to predict occult metastasis in the lateral neck is needed. METHODS: From January 2010 to January 2022, patients who were diagnosed with MTC and underwent primary surgery at our hospital were retrospectively reviewed. We collected the patients' baseline characteristics, surgical procedure, and rescored the ultrasound images of the primary lesions using American College of Radiology (ACR) Thyroid Imaging, Reporting and Data System (TI-RADS). Regularized logistic regression, 5-fold cross-validation and decision curve analysis was applied for lateral lymph node metastasis (LLNM) model's development and validation. Then, we tested the predictive ability of the LLNM model for occult LLNM in cN0-1a patients. RESULTS: A total of 218 patients were enrolled. Five baseline characteristics and two TI-RADS features were identified as high-risk factors for LLNM: gender, baseline calcitonin (Ctn), tumor size, multifocality, and central lymph node (CLN) status, as well as TI-RADS margin and level. A LLNM model was developed and showed a good discrimination with 5-fold cross-validation mean area under curve (AUC) = 0.92 ± 0.03 in the test dataset. Among cN0-1a patients, our LLNM model achieved an AUC of 0.91 (95% CI, 0.88-0.94) for predicting occult LLNM, which was significantly higher than the AUCs of baseline Ctn (0.83) and CLN status (0.64). CONCLUSIONS: We developed a LLNM prediction model for MTC using machine learning based on clinical baseline characteristics and TI-RADS. Our model can predict occult LLNM for cN0-1a patients more accurately, then benefit the decision of prophylactic LND.

The model for predicting the central lymph node metastasis in cN0 papillary thyroid microcarcinoma with Hashimoto's thyroiditis.

Background: The relationship between Hashimoto's thyroiditis (HT) and papillary thyroid microcarcinoma (PTMC) is controversial. These include central lymph node metastasis (CLNM), which affects the prognosis of PTMC patients. This study aimed to establish a predictive model combining ultrasonography and clinicopathological features to accurately evaluate latent CLNM in PTMC patients with HT at the clinical lymph node-negative (cN0) stage. Methods: In this study, 1102 PTMC patients who received thyroidectomy and central cervical lymph node dissection (CLND) from the First Affiliated Hospital of Shandong First Medical University from January 2021 to December 2022 and the 960th Hospital of PLA from January 2021 to December 2022 were jointly collected. The clinical differences between PTMCs with HT and those without HT were compared. A total of 373 PTMCs with HT in cN0 were randomly divided into a training cohort and a validation cohort. By analyzing and screening the risk factors of CLNM, a nomogram model was established and verified. The predictive performance was measured by the receiver operating characteristic (ROC) curve, calibration curve, and clinical decision curve analysis (DCA). Results: The ratio of central lymph node metastasis (CLNMR) in PTMCs with HT was 0.0% (0.0%, 15.0%) and 7.7% (0.0%, 40.0%) in the non-HT group (P<0.001). Multivariate logistic regression analysis showed that age, gender, calcification, adjacent to trachea or capsule, and TPOAB were predictors of CLNM in PTMCs with HT. The areas under the curve (AUC) of the prediction models in the training cohort and the validation cohort were 0.835 and 0.825, respectively, which showed good differentiation ability. DCA indicates that the prediction model also has high net benefit and clinical practical value. Conclusion: This study found that CLN involvement was significantly reduced in PTMC patients with HT, suggesting that different methods should be used to predict CLNM in PTMC patients with HT and without HT, to more accurately assist preoperative clinical evaluation. The actual CLNM situation of PTMCs with HT in cN0 can be accurately predicted by the combination of ultrasonography and clinicopathological features.

The use of longitudinal CT-based radiomics and clinicopathological features predicts the pathological complete response of metastasized axillary lymph nodes in breast cancer.

BACKGROUND: Accurate assessment of axillary status after neoadjuvant therapy for breast cancer patients with axillary lymph node metastasis is important for the selection of appropriate subsequent axillary treatment decisions. Our objectives were to accurately predict whether the breast cancer patients with axillary lymph node metastases could achieve axillary pathological complete response (pCR). METHODS: We collected imaging data to extract longitudinal CT image features before and after neoadjuvant chemotherapy (NAC), analyzed the correlation between radiomics and clinicopathological features, and developed models to predict whether patients with axillary lymph node metastasis can achieve axillary pCR after NAC. The clinical utility of the models was determined via decision curve analysis (DCA). Subgroup analyses were also performed. Then, a nomogram was developed based on the model with the best predictive efficiency and clinical utility and was validated using the calibration plots. RESULTS: A total of 549 breast cancer patients with metastasized axillary lymph nodes were enrolled in this study. 42 independent radiomics features were selected from LASSO regression to construct a logistic regression model with clinicopathological features (LR radiomics-clinical combined model). The AUC of the LR radiomics-clinical combined model prediction performance was 0.861 in the training set and 0.891 in the testing set. For the HR + /HER2 - , HER2 + , and Triple negative subtype, the LR radiomics-clinical combined model yields the best prediction AUCs of 0.756, 0.812, and 0.928 in training sets, and AUCs of 0.757, 0.777 and 0.838 in testing sets, respectively. CONCLUSIONS: The combination of radiomics features and clinicopathological characteristics can effectively predict axillary pCR status in NAC breast cancer patients.

MRI Texture Analysis of Inguinal Lymph Node Metastasis in Vulvar Cancer - Associations With Histopathology.

BACKGROUND/AIM: Texture analysis is a quantitative imaging technique that provides novel biomarkers beyond conventional image reading. This study aimed to investigate the correlation between texture parameters and histopathological features of lymph nodes in patients with vulvar cancer. PATIENTS AND METHODS: Overall, nine female patients (mean age 70.1±13.4 years, range=39-87 years) were included in the analysis. All patients had squamous cell carcinomas and underwent upfront surgery with inguinal lymph node resection. Immunohistochemical assessment was performed using several markers of the epithelial-mesenchymal transition. The presurgical magnetic resonance imaging (MRI) was analyzed with the MaZda package. RESULTS: In discrimination analysis, several parameters derived from T1-weighted images showed statistically significant differences between non-metastatic and metastatic lymph nodes. The highest statistical significance was reached by the texture feature "S(0,3)InvDfMom" (p=0.016). In correlation analysis, significant associations were found between MRI texture parameters derived from both T1-weighted and T2-weighted images and the investigated histopathological features. Notably, S(0,3)InvDfMom derived from T1-weighted images highly correlated with the Vimentin-score (r=0.908, p=0.001). CONCLUSION: Several associations between MRI texture analysis and immunohistochemical parameters were identified in metastasized lymph nodes of cases with vulvar cancer.

A deep learning-based radiomics model for predicting lymph node status from lung adenocarcinoma.

OBJECTIVES: At present, there are many limitations in the evaluation of lymph node metastasis of lung adenocarcinoma. Currently, there is a demand for a safe and accurate method to predict lymph node metastasis of lung cancer. In this study, radiomics was used to accurately predict the lymph node status of lung adenocarcinoma patients based on contrast-enhanced CT. METHODS: A total of 503 cases that fulfilled the analysis requirements were gathered from two distinct hospitals. Among these, 287 patients exhibited lymph node metastasis (LNM +) while 216 patients were confirmed to be without lymph node metastasis (LNM-). Using both traditional and deep learning methods, 22,318 features were extracted from the segmented images of each patient's enhanced CT. Then, the spearman test and the least absolute shrinkage and selection operator were used to effectively reduce the dimension of the feature data, enabling us to focus on the most pertinent features and enhance the overall analysis. Finally, the classification model of lung adenocarcinoma lymph node metastasis was constructed by machine learning algorithm. The Accuracy, AUC, Specificity, Precision, Recall and F1 were used to evaluate the efficiency of the model. RESULTS: By incorporating a comprehensively selected set of features, the extreme gradient boosting method (XGBoost) effectively distinguished the status of lymph nodes in patients with lung adenocarcinoma. The Accuracy, AUC, Specificity, Precision, Recall and F1 of the prediction model performance on the external test set were 0.765, 0.845, 0.705, 0.784, 0.811 and 0.797, respectively. Moreover, the decision curve analysis, calibration curve and confusion matrix of the model on the external test set all indicated the stability and accuracy of the model. CONCLUSIONS: Leveraging enhanced CT images, our study introduces a noninvasive classification prediction model based on the extreme gradient boosting method. This approach exhibits remarkable precision in identifying the lymph node status of lung adenocarcinoma patients, offering a safe and accurate alternative to invasive procedures. By providing clinicians with a reliable tool for diagnosing and assessing disease progression, our method holds the potential to significantly improve patient outcomes and enhance the overall quality of clinical practice.

Comparison of Doppler Imaging and Microvascular Imaging in Cervical Lymph Node Blood Flow Analysis.

Cervical lymph node metastasis is an important determinant of cancer stage and the selection of an appropriate treatment plan for patients with head and neck cancer. Therefore, metastatic cervical lymph nodes should be effectively differentiated from lymphoma, tuberculous lymphadenitis, and other benign lymphadenopathies. The aim of this work is to describe the performance of Doppler ultrasound and superb microvascular imaging (SMI) in evaluating blood flow information of cervical lymph nodes. In addition, the features of flow imaging in metastatic lymph nodes, lymphoma, and tuberculous lymphadenitis were described. Compared with Doppler ultrasound, SMI, the latest blood flow imaging technology, could detect more blood flow signals because the sensitivity, specificity, and accuracy of SMI in the diagnosis of cervical lymph node disease were higher. This article summarizes the value of Doppler ultrasound and SMI in evaluating cervical lymph node diseases and focuses on the diagnostic performance of SMI.

Use superb microvascular imaging to diagnose and predict metastatic cervical lymph nodes in patients with papillary thyroid carcinoma.

PURPOSE: Papillary thyroid carcinoma (PTC) with metastatic lymph nodes (LNs) is closely associated with disease recurrence. This study accessed the value of superb microvascular imaging (SMI) in the diagnosis and prediction of metastatic cervical LNs in patients with PTC. METHODS: A total of 183 cervical LNs (103 metastatic and 80 reactive) from 116 patients with PTC were analysed. Metastatic cervical LNs were confirmed by pathology or/and cytology; reactive cervical LNs were confirmed by pathology or clinical features. The characteristic of conventional ultrasound (US) was extracted using univariate and multivariate analyses. The diagnostic performance of US and SMI were compared using the area under the receiver operating curve (AUC) with corresponding sensitivity and specificity. A nomogram was developed to predict metastatic LNs in patients with PTC, based on multivariate analyses. RESULTS: L/S < 2, ill-defined border, absence of hilum, isoechoic or hyperechoic, heterogeneous internal echo, peripheral or mixed vascular pattern on color Doppler flow imaging (CDFI) and SMI, and a larger SMI vascular index appeared more frequently in metastatic LNs in the training datasets than in reactive LNs (P < 0.05). The diagnostic sensitivity, specificity and accuracy of SMI vs US are 94.4% and 87.3%, 79.3% and 69.3%, and 87.6% and 79.1%, respectively; SMI combined with US exhibited a higher AUC [0.926 (0.877-0.975)] than US only [0.829 (0.759-0.900)]. L/S < 2, peripheral or mixed vascular type on CDFI, and peripheral or mixed vascular types on SMI were independent predictors of metastatic LNs with PTC. The nomogram based on these three parameters exhibited excellent discrimination, with an AUC of 0.926. CONCLUSION: SMI was superior to US in diagnosing metastatic LNs in PTC. US combined with SMI significantly improved the diagnostic accuracy of metastatic cervical LNs with PTC. SMI is efficacious for differentiating and predicting metastatic cervical LNs.